Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems

Wang, Yixuan, Huang, Chao ORCID: 0000-0002-9300-1787 and Zhu, Qi
(2020) Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems.

[img] Text
2008.06162v2.pdf - Submitted Version

Download (511kB) | Preview


Neural networks have been increasingly applied for control in learning-enabled cyber-physical systems (LE-CPSs) and demonstrated great promises in improving system performance and efficiency, as well as reducing the need for complex physical models. However, the lack of safety guarantees for such neural network based controllers has significantly impeded their adoption in safety-critical CPSs. In this work, we propose a controller adaptation approach that automatically switches among multiple controllers, including neural network controllers, to guarantee system safety and improve energy efficiency. Our approach includes two key components based on formal methods and machine learning. First, we approximate each controller with a Bernstein-polynomial based hybrid system model under bounded disturbance, and compute a safe invariant set for each controller based on its corresponding hybrid system. Intuitively, the invariant set of a controller defines the state space where the system can always remain safe under its control. The union of the controllers' invariants sets then define a safe adaptation space that is larger than (or equal to) that of each controller. Second, we develop a deep reinforcement learning method to learn a controller switching strategy for reducing the control/actuation energy cost, while with the help of a safety guard rule, ensuring that the system stays within the safe space. Experiments on a linear adaptive cruise control system and a non-linear Van der Pol's oscillator demonstrate the effectiveness of our approach on energy saving and safety enhancement.

Item Type: Article
Additional Information: Accepted by 39th International Conference On Computer Aided Design(ICCAD 2020)
Uncontrolled Keywords: eess.SY, eess.SY, cs.SY
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 14 Sep 2021 13:22
Last Modified: 06 May 2022 09:10
Related URLs: